How social information can improve estimation accuracy in human groups

TL;DR

This paper investigates how social information influences individual and collective estimation accuracy, demonstrating that social influence can enhance group performance and proposing a model to optimize information sharing.

Contribution

It introduces a model of collective estimation that quantifies the impact of social influence and information quality on group accuracy, supported by experiments in France and Japan.

Findings

Social influence improves collective estimation accuracy.

Distribution of estimates approximates a Cauchy distribution with little prior knowledge.

Providing controlled incorrect information can counteract biases and enhance performance.

Abstract

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the question of how individuals use social information, and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation, to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities, and thereby improve collective performance.